Redundancy Allocation of Partitioned Linear Block Codes

TL;DR

This paper investigates how to optimally allocate redundancy in partitioned linear block codes to improve memory reliability by reducing decoding failures caused by defects and errors.

Contribution

It introduces a method to determine the optimal redundancy split in PLBC for defect masking and error correction, supported by simulations and theoretical bounds.

Findings

PLBC significantly reduces decoding failure probability.

Derived upper bounds help estimate optimal redundancy allocation.

Simulation results confirm the effectiveness of the proposed allocation.

Abstract

Most memories suffer from both permanent defects and intermittent random errors. The partitioned linear block codes (PLBC) were proposed by Heegard to efficiently mask stuck-at defects and correct random errors. The PLBC have two separate redundancy parts for defects and random errors. In this paper, we investigate the allocation of redundancy between these two parts. The optimal redundancy allocation will be investigated using simulations and the simulation results show that the PLBC can significantly reduce the probability of decoding failure in memory with defects. In addition, we will derive the upper bound on the probability of decoding failure of PLBC and estimate the optimal redundancy allocation using this upper bound. The estimated redundancy allocation matches the optimal redundancy allocation well.